Buffer and Spatial Join (POI Search)

Based on our analysis of trip data, we have identified the top five bike stations in Seattle City. We are currently investigating why these stations are so popular. Our initial hypothesis is that the area surrounding these stations may have notable landmarks that attract tourists and contribute to their high usage.

To use the workflow, download and run it on the KNIME Analytics Platform. For optimal performance using the Geospatial Analytics extension, make sure you are using version 4.7.4 or higher.

The input data has been saved in the workflow data area, simply execute the component "Read Dataset" to obtain the necessary data.

We will use the landmark points on the map to determine how many fall within a 250-meter buffer zone around the top 5 bike stations. We anticipate a significant number of points to fall within these areas.

To create a 250-meter area around each bike station, we first convert the latitude and longitude columns from the bike station CSV into a geometry point feature. We then use the Buffer node configuring the distance as "250", the distance unit as "Meter", and flagging "Keep CRS from input table".

It's time to link up the landmark points within the bike station area. We'll do this using the Spatial Join node and selecting "Within" as the match mode. This means that only the landmark points that intersect with the interior of the bike station buffered area will be included, excluding the area boundaries.

We converted the buffered area around the bike station from a polygon to a line geometry type creating an unfilled area.
Then, we combine the filtered Landmark points with the buffered bike station areas and display the results using the Geospatial View node. You can review the view to confirm or reject our hypothesis.

External resources

• Cycle Share Dataset
• Landmarks